Intelligent Bioelectrochemical Systems Enhanced by Machine Learning

PhD Research Project – University of São Paulo (USP)

OVERVIEW: This doctoral research investigates the integration of Bioelectrochemical Systems (BES) with machine learning methodologies to enhance predictive performance, system optimization, and electrochemical understanding. The project focuses on microbial fuel cells (MFCs) and microbial electrolysis cells (MECs), exploring electron transfer mechanisms, catalytic behavior, and performance scalability. By combining experimental electrochemistry with data-driven modeling, the research aims to establish hybrid predictive frameworks capable of describing and optimizing bioelectrochemical processes under varying operational conditions.

Research Objectives

• Analyze electrochemical performance of MFCs and MECs

• Model current density, voltage behavior, and internal resistance

• Develop predictive models using machine learning algorithms

• Apply explainable AI techniques to interpret electrochemical patterns

• Evaluate sustainability and energy efficiency metrics

Methodology

• Experimental setup of microbial electrochemical systems

• Electrochemical characterization (CV, polarization curves, power density analysis)

• Real-time data acquisition systems

• Machine learning modeling (regression and predictive frameworks)

• Model interpretability using feature importance analysis

Innovation

This research proposes a hybrid approach that integrates physicochemical modeling with machine learning, bridging experimental electrochemistry and artificial intelligence. The framework enhances predictive reliability and contributes to the development of intelligent, self-optimizing bioelectrochemical platforms.

Impact

• Sustainable energy generation

• Hydrogen production optimization

• Environmental remediation technologies

• AI-driven scientific modeling in electrochemical systems

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